{
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  "metadata": {
    "colab": {
      "name": "KerasClassification",
      "provenance": [],
      "authorship_tag": "ABX9TyPjNkFdjrFdhXMEvNoTCqPw",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/KerasClassification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WawFfZmO3nDq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "10df303b-d5c5-43eb-aa1b-375b0a702bd4"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sfB0x_-e5b1F",
        "colab_type": "text"
      },
      "source": [
        "#Tumour Classification : Malignent/Benign Problem"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qm-iSDUk5Tyy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('cancer_classification.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0KlRU5k1566B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1 = df.copy()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vt48Yn-559x5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "17c095ab-7570-4eca-8247-2177a06f9994"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>radius error</th>\n",
              "      <th>texture error</th>\n",
              "      <th>perimeter error</th>\n",
              "      <th>area error</th>\n",
              "      <th>smoothness error</th>\n",
              "      <th>compactness error</th>\n",
              "      <th>concavity error</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>symmetry error</th>\n",
              "      <th>fractal dimension error</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <th>benign_0__mal_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean radius  mean texture  ...  worst fractal dimension  benign_0__mal_1\n",
              "0        17.99         10.38  ...                  0.11890                0\n",
              "1        20.57         17.77  ...                  0.08902                0\n",
              "2        19.69         21.25  ...                  0.08758                0\n",
              "3        11.42         20.38  ...                  0.17300                0\n",
              "4        20.29         14.34  ...                  0.07678                0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LyG3GwTM6CfP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "outputId": "18ada097-9213-477b-ddad-0316c23e5d6f"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 31 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   mean radius              569 non-null    float64\n",
            " 1   mean texture             569 non-null    float64\n",
            " 2   mean perimeter           569 non-null    float64\n",
            " 3   mean area                569 non-null    float64\n",
            " 4   mean smoothness          569 non-null    float64\n",
            " 5   mean compactness         569 non-null    float64\n",
            " 6   mean concavity           569 non-null    float64\n",
            " 7   mean concave points      569 non-null    float64\n",
            " 8   mean symmetry            569 non-null    float64\n",
            " 9   mean fractal dimension   569 non-null    float64\n",
            " 10  radius error             569 non-null    float64\n",
            " 11  texture error            569 non-null    float64\n",
            " 12  perimeter error          569 non-null    float64\n",
            " 13  area error               569 non-null    float64\n",
            " 14  smoothness error         569 non-null    float64\n",
            " 15  compactness error        569 non-null    float64\n",
            " 16  concavity error          569 non-null    float64\n",
            " 17  concave points error     569 non-null    float64\n",
            " 18  symmetry error           569 non-null    float64\n",
            " 19  fractal dimension error  569 non-null    float64\n",
            " 20  worst radius             569 non-null    float64\n",
            " 21  worst texture            569 non-null    float64\n",
            " 22  worst perimeter          569 non-null    float64\n",
            " 23  worst area               569 non-null    float64\n",
            " 24  worst smoothness         569 non-null    float64\n",
            " 25  worst compactness        569 non-null    float64\n",
            " 26  worst concavity          569 non-null    float64\n",
            " 27  worst concave points     569 non-null    float64\n",
            " 28  worst symmetry           569 non-null    float64\n",
            " 29  worst fractal dimension  569 non-null    float64\n",
            " 30  benign_0__mal_1          569 non-null    int64  \n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 137.9 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNIINKP-7Tk6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#No Null values. "
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dWp6DM057cYx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d295b856-8ed2-4449-df63-b35729647254"
      },
      "source": [
        "df.describe().T"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>mean radius</th>\n",
              "      <td>569.0</td>\n",
              "      <td>14.127292</td>\n",
              "      <td>3.524049</td>\n",
              "      <td>6.981000</td>\n",
              "      <td>11.700000</td>\n",
              "      <td>13.370000</td>\n",
              "      <td>15.780000</td>\n",
              "      <td>28.11000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean texture</th>\n",
              "      <td>569.0</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>39.28000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean perimeter</th>\n",
              "      <td>569.0</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>188.50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean area</th>\n",
              "      <td>569.0</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>2501.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean smoothness</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.16340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean compactness</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.34540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean concavity</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.42680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean concave points</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.20120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean symmetry</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>0.30400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.062798</td>\n",
              "      <td>0.007060</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>0.057700</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.066120</td>\n",
              "      <td>0.09744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius error</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.405172</td>\n",
              "      <td>0.277313</td>\n",
              "      <td>0.111500</td>\n",
              "      <td>0.232400</td>\n",
              "      <td>0.324200</td>\n",
              "      <td>0.478900</td>\n",
              "      <td>2.87300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture error</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.216853</td>\n",
              "      <td>0.551648</td>\n",
              "      <td>0.360200</td>\n",
              "      <td>0.833900</td>\n",
              "      <td>1.108000</td>\n",
              "      <td>1.474000</td>\n",
              "      <td>4.88500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter error</th>\n",
              "      <td>569.0</td>\n",
              "      <td>2.866059</td>\n",
              "      <td>2.021855</td>\n",
              "      <td>0.757000</td>\n",
              "      <td>1.606000</td>\n",
              "      <td>2.287000</td>\n",
              "      <td>3.357000</td>\n",
              "      <td>21.98000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area error</th>\n",
              "      <td>569.0</td>\n",
              "      <td>40.337079</td>\n",
              "      <td>45.491006</td>\n",
              "      <td>6.802000</td>\n",
              "      <td>17.850000</td>\n",
              "      <td>24.530000</td>\n",
              "      <td>45.190000</td>\n",
              "      <td>542.20000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness error</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.007041</td>\n",
              "      <td>0.003003</td>\n",
              "      <td>0.001713</td>\n",
              "      <td>0.005169</td>\n",
              "      <td>0.006380</td>\n",
              "      <td>0.008146</td>\n",
              "      <td>0.03113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness error</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.025478</td>\n",
              "      <td>0.017908</td>\n",
              "      <td>0.002252</td>\n",
              "      <td>0.013080</td>\n",
              "      <td>0.020450</td>\n",
              "      <td>0.032450</td>\n",
              "      <td>0.13540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity error</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.031894</td>\n",
              "      <td>0.030186</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015090</td>\n",
              "      <td>0.025890</td>\n",
              "      <td>0.042050</td>\n",
              "      <td>0.39600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points error</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.011796</td>\n",
              "      <td>0.006170</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007638</td>\n",
              "      <td>0.010930</td>\n",
              "      <td>0.014710</td>\n",
              "      <td>0.05279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry error</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.020542</td>\n",
              "      <td>0.008266</td>\n",
              "      <td>0.007882</td>\n",
              "      <td>0.015160</td>\n",
              "      <td>0.018730</td>\n",
              "      <td>0.023480</td>\n",
              "      <td>0.07895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal dimension error</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.003795</td>\n",
              "      <td>0.002646</td>\n",
              "      <td>0.000895</td>\n",
              "      <td>0.002248</td>\n",
              "      <td>0.003187</td>\n",
              "      <td>0.004558</td>\n",
              "      <td>0.02984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst radius</th>\n",
              "      <td>569.0</td>\n",
              "      <td>16.269190</td>\n",
              "      <td>4.833242</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>18.790000</td>\n",
              "      <td>36.04000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst texture</th>\n",
              "      <td>569.0</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>49.54000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst perimeter</th>\n",
              "      <td>569.0</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>251.20000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst area</th>\n",
              "      <td>569.0</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>4254.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst smoothness</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.132369</td>\n",
              "      <td>0.022832</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.22260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst compactness</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>1.05800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst concavity</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>1.25200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst concave points</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.29100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst symmetry</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.66380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.083946</td>\n",
              "      <td>0.018061</td>\n",
              "      <td>0.055040</td>\n",
              "      <td>0.071460</td>\n",
              "      <td>0.080040</td>\n",
              "      <td>0.092080</td>\n",
              "      <td>0.20750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>benign_0__mal_1</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.627417</td>\n",
              "      <td>0.483918</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         count        mean  ...          75%         max\n",
              "mean radius              569.0   14.127292  ...    15.780000    28.11000\n",
              "mean texture             569.0   19.289649  ...    21.800000    39.28000\n",
              "mean perimeter           569.0   91.969033  ...   104.100000   188.50000\n",
              "mean area                569.0  654.889104  ...   782.700000  2501.00000\n",
              "mean smoothness          569.0    0.096360  ...     0.105300     0.16340\n",
              "mean compactness         569.0    0.104341  ...     0.130400     0.34540\n",
              "mean concavity           569.0    0.088799  ...     0.130700     0.42680\n",
              "mean concave points      569.0    0.048919  ...     0.074000     0.20120\n",
              "mean symmetry            569.0    0.181162  ...     0.195700     0.30400\n",
              "mean fractal dimension   569.0    0.062798  ...     0.066120     0.09744\n",
              "radius error             569.0    0.405172  ...     0.478900     2.87300\n",
              "texture error            569.0    1.216853  ...     1.474000     4.88500\n",
              "perimeter error          569.0    2.866059  ...     3.357000    21.98000\n",
              "area error               569.0   40.337079  ...    45.190000   542.20000\n",
              "smoothness error         569.0    0.007041  ...     0.008146     0.03113\n",
              "compactness error        569.0    0.025478  ...     0.032450     0.13540\n",
              "concavity error          569.0    0.031894  ...     0.042050     0.39600\n",
              "concave points error     569.0    0.011796  ...     0.014710     0.05279\n",
              "symmetry error           569.0    0.020542  ...     0.023480     0.07895\n",
              "fractal dimension error  569.0    0.003795  ...     0.004558     0.02984\n",
              "worst radius             569.0   16.269190  ...    18.790000    36.04000\n",
              "worst texture            569.0   25.677223  ...    29.720000    49.54000\n",
              "worst perimeter          569.0  107.261213  ...   125.400000   251.20000\n",
              "worst area               569.0  880.583128  ...  1084.000000  4254.00000\n",
              "worst smoothness         569.0    0.132369  ...     0.146000     0.22260\n",
              "worst compactness        569.0    0.254265  ...     0.339100     1.05800\n",
              "worst concavity          569.0    0.272188  ...     0.382900     1.25200\n",
              "worst concave points     569.0    0.114606  ...     0.161400     0.29100\n",
              "worst symmetry           569.0    0.290076  ...     0.317900     0.66380\n",
              "worst fractal dimension  569.0    0.083946  ...     0.092080     0.20750\n",
              "benign_0__mal_1          569.0    0.627417  ...     1.000000     1.00000\n",
              "\n",
              "[31 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ey8E281u7fcU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "cf9810ba-5afb-4c84-a87c-fba11ff9ff37"
      },
      "source": [
        "sns.countplot(x='benign_0__mal_1',data = df) #counts the number of 0's and 1's in this categorical column."
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efed8cc7f98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ba2ZhQCB79BZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        },
        "outputId": "4e5ace02-ff65-46e5-ee78-16a0dcf36fb4"
      },
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "sns.heatmap(df.corr())"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efed8c02470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZzZ3nra8eOD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "284da2b4-55d4-4368-87bc-2f6d7ff7eb09"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "\n",
        "df.corr()['benign_0__mal_1'].sort_values().plot(kind='bar')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efed8c08c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QvrAnObb85L6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df.drop('benign_0__mal_1',axis=1).values"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWA6wmXf_e2d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = df['benign_0__mal_1'].values"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IsWCZWp3_meD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Z4gwRPHATc-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=101)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LRenOuW7BC-E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lu0c0uPJBRkn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler = MinMaxScaler()"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JpYXc7FoBdV3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = scaler.fit_transform(X_train)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LbFqkOMBBrmF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0srvacZBCa5A",
        "colab_type": "text"
      },
      "source": [
        "#Part 2 : Dealing with overfitting (early stop | layer drop) \n",
        "## ANd finally model evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9wNOrnSnCAxd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9H8Zd5kyDauI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "73d38eb3-3d67-4c3a-c4de-79cb8f327449"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(426, 30)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I9G8nvQWDf96",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#30 Features. \n",
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(30,activation='relu'))\n",
        "\n",
        "model.add(Dense(15,activation='relu'))\n",
        "\n",
        "#BINARY CLASS. SO FINAL OUTPUT SHOULD BE 0/1\n",
        "model.add(Dense(1,activation='sigmoid'))"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P6Upy_ZuEnXa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss='binary_crossentropy',optimizer='adam')"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cHC26npFE8t3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "54cf5523-1698-4dcb-cf29-80490a39121c"
      },
      "source": [
        "model.fit(x=X_train,y=y_train,epochs=600,validation_data=(X_test,y_test))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/600\n",
            "14/14 [==============================] - 0s 14ms/step - loss: 0.6805 - val_loss: 0.6489\n",
            "Epoch 2/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.6357 - val_loss: 0.6117\n",
            "Epoch 3/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.5931 - val_loss: 0.5663\n",
            "Epoch 4/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.5454 - val_loss: 0.5152\n",
            "Epoch 5/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.4926 - val_loss: 0.4571\n",
            "Epoch 6/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.4379 - val_loss: 0.4008\n",
            "Epoch 7/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3860 - val_loss: 0.3488\n",
            "Epoch 8/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3402 - val_loss: 0.3060\n",
            "Epoch 9/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3004 - val_loss: 0.2656\n",
            "Epoch 10/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2649 - val_loss: 0.2350\n",
            "Epoch 11/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2368 - val_loss: 0.2123\n",
            "Epoch 12/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2165 - val_loss: 0.1931\n",
            "Epoch 13/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2010 - val_loss: 0.1794\n",
            "Epoch 14/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1908 - val_loss: 0.1670\n",
            "Epoch 15/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1776 - val_loss: 0.1608\n",
            "Epoch 16/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1660 - val_loss: 0.1509\n",
            "Epoch 17/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1556 - val_loss: 0.1439\n",
            "Epoch 18/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1482 - val_loss: 0.1385\n",
            "Epoch 19/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1407 - val_loss: 0.1371\n",
            "Epoch 20/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1351 - val_loss: 0.1312\n",
            "Epoch 21/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1291 - val_loss: 0.1253\n",
            "Epoch 22/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1223 - val_loss: 0.1235\n",
            "Epoch 23/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1165 - val_loss: 0.1220\n",
            "Epoch 24/600\n",
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            "Epoch 137/600\n",
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            "Epoch 138/600\n",
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            "Epoch 139/600\n",
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            "Epoch 140/600\n",
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            "Epoch 141/600\n",
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            "Epoch 142/600\n",
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            "Epoch 143/600\n",
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            "Epoch 144/600\n",
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            "Epoch 145/600\n",
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            "Epoch 146/600\n",
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            "Epoch 147/600\n",
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            "Epoch 148/600\n",
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            "Epoch 149/600\n",
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            "Epoch 150/600\n",
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            "Epoch 151/600\n",
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            "Epoch 152/600\n",
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            "Epoch 153/600\n",
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            "Epoch 154/600\n",
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            "Epoch 155/600\n",
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            "Epoch 156/600\n",
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            "Epoch 157/600\n",
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            "Epoch 158/600\n",
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            "Epoch 159/600\n",
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            "Epoch 160/600\n",
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            "Epoch 161/600\n",
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            "Epoch 162/600\n",
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            "Epoch 163/600\n",
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            "Epoch 164/600\n",
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            "Epoch 165/600\n",
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            "Epoch 166/600\n",
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            "Epoch 167/600\n",
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            "Epoch 168/600\n",
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            "Epoch 169/600\n",
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            "Epoch 170/600\n",
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            "Epoch 171/600\n",
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            "Epoch 172/600\n",
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            "Epoch 173/600\n",
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            "Epoch 174/600\n",
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            "Epoch 175/600\n",
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            "Epoch 176/600\n",
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            "Epoch 177/600\n",
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            "Epoch 178/600\n",
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            "Epoch 179/600\n",
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            "Epoch 180/600\n",
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            "Epoch 181/600\n",
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            "Epoch 182/600\n",
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            "Epoch 183/600\n",
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            "Epoch 184/600\n",
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            "Epoch 185/600\n",
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            "Epoch 186/600\n",
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            "Epoch 187/600\n",
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            "Epoch 188/600\n",
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            "Epoch 189/600\n",
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            "Epoch 190/600\n",
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            "Epoch 191/600\n",
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            "Epoch 192/600\n",
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            "Epoch 193/600\n",
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            "Epoch 270/600\n",
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            "Epoch 300/600\n",
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            "Epoch 310/600\n",
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            "Epoch 312/600\n",
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            "Epoch 313/600\n",
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            "Epoch 315/600\n",
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            "Epoch 316/600\n",
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            "Epoch 318/600\n",
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            "Epoch 320/600\n",
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            "Epoch 329/600\n",
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            "Epoch 331/600\n",
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            "Epoch 350/600\n",
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            "Epoch 351/600\n",
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            "Epoch 352/600\n",
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            "Epoch 353/600\n",
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            "Epoch 354/600\n",
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            "Epoch 355/600\n",
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            "Epoch 356/600\n",
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            "Epoch 357/600\n",
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            "Epoch 358/600\n",
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            "Epoch 359/600\n",
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            "Epoch 360/600\n",
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            "Epoch 361/600\n",
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            "Epoch 362/600\n",
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            "Epoch 533/600\n",
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            "Epoch 534/600\n",
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            "Epoch 536/600\n",
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            "Epoch 537/600\n",
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            "Epoch 538/600\n",
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            "Epoch 539/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0139 - val_loss: 0.2174\n",
            "Epoch 540/600\n",
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            "Epoch 541/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0149 - val_loss: 0.2336\n",
            "Epoch 542/600\n",
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            "Epoch 544/600\n",
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            "Epoch 546/600\n",
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            "Epoch 547/600\n",
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            "Epoch 549/600\n",
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            "Epoch 550/600\n",
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            "Epoch 552/600\n",
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            "Epoch 553/600\n",
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            "Epoch 554/600\n",
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            "Epoch 559/600\n",
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            "Epoch 560/600\n",
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            "Epoch 561/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0149 - val_loss: 0.2295\n",
            "Epoch 562/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0149 - val_loss: 0.2196\n",
            "Epoch 563/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0173 - val_loss: 0.2852\n",
            "Epoch 564/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0128 - val_loss: 0.2258\n",
            "Epoch 565/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0132 - val_loss: 0.2444\n",
            "Epoch 566/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0142 - val_loss: 0.2186\n",
            "Epoch 567/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0209 - val_loss: 0.2474\n",
            "Epoch 568/600\n",
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            "Epoch 569/600\n",
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            "Epoch 570/600\n",
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            "Epoch 571/600\n",
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            "Epoch 572/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0123 - val_loss: 0.2363\n",
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            "Epoch 578/600\n",
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            "Epoch 580/600\n",
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            "Epoch 581/600\n",
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            "Epoch 583/600\n",
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            "Epoch 584/600\n",
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            "Epoch 586/600\n",
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            "Epoch 587/600\n",
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            "Epoch 588/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0117 - val_loss: 0.2543\n",
            "Epoch 589/600\n",
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            "Epoch 590/600\n",
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            "Epoch 591/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0116 - val_loss: 0.2648\n",
            "Epoch 592/600\n",
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            "Epoch 593/600\n",
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            "Epoch 594/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0136 - val_loss: 0.2660\n",
            "Epoch 595/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0118 - val_loss: 0.2405\n",
            "Epoch 596/600\n",
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            "Epoch 597/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0128 - val_loss: 0.2571\n",
            "Epoch 598/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0101 - val_loss: 0.2677\n",
            "Epoch 599/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0107 - val_loss: 0.2338\n",
            "Epoch 600/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0118 - val_loss: 0.2697\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7efe8d40d630>"
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          },
          "metadata": {
            "tags": []
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          "execution_count": 23
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    {
      "cell_type": "code",
      "metadata": {
        "id": "SKr-LEPhFjZM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loss = pd.DataFrame(model.history.history)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q09Vvtb8HPsU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "610dac96-3fef-46c1-af64-51173c29e056"
      },
      "source": [
        "loss.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>val_loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.680457</td>\n",
              "      <td>0.648884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.635735</td>\n",
              "      <td>0.611717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.593107</td>\n",
              "      <td>0.566347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.545353</td>\n",
              "      <td>0.515194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.492626</td>\n",
              "      <td>0.457080</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       loss  val_loss\n",
              "0  0.680457  0.648884\n",
              "1  0.635735  0.611717\n",
              "2  0.593107  0.566347\n",
              "3  0.545353  0.515194\n",
              "4  0.492626  0.457080"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 25
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    {
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      "metadata": {
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        "colab": {
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          "height": 282
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        "outputId": "1f766447-eba4-4514-d2a4-b0d2741a4676"
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      "source": [
        "loss.plot()"
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      "execution_count": 26,
      "outputs": [
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              "<matplotlib.axes._subplots.AxesSubplot at 0x7efe885fdb38>"
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        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLLccIOlHZZz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#This is happening because we've used 600 epochs and that's a lot : OVERFITTING"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHaUMck4j-9i",
        "colab_type": "text"
      },
      "source": [
        "#Call-backs and Early stopping (to avoid this error)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_9V5-b4ejt8S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#30 Features. \n",
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(30,activation='relu'))\n",
        "\n",
        "model.add(Dense(15,activation='relu'))\n",
        "\n",
        "#BINARY CLASS. SO FINAL OUTPUT SHOULD BE 0/1\n",
        "model.add(Dense(1,activation='sigmoid'))\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam')"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TAa0iCPTkNXb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r506p_9_kUg1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# help(EarlyStopping)\n",
        "#  Assuming the goal of a training is to minimize the loss. With this, the\n",
        "#  |  metric to be monitored would be 'loss', and mode would be 'min'. A\n",
        "#  |  `model.fit()` training loop will check at end of every epoch whether\n",
        "#  |  the loss is no longer decreasing, considering the `min_delta` and\n",
        "#  |  `patience` if applicable. Once it's found no longer decreasing,\n",
        "#  |  `model.stop_training` is marked True and the training terminates.\n",
        "#  |  We'll pick the metric as validation loss."
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ObxGzQAwkWgH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "early_stop = EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=25)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWR2TFawliNP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#note if our metric was accuracy , we'd want to maximize it. \n",
        "#if our metric was loss , we'd want to minimize it. \n",
        "\n",
        "# mode = auto, automoatically picks the mode based on the string. \n",
        "\n",
        "#patience = 25 means that we'll be waiting 25 epochs after the loss minimization just to make sure. \n",
        "# model.compile(loss='binary_crossentropy',optimizer='adam')"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FZ0qTWh7l7cb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "564bf42a-5fe5-49b9-bfca-418255387b7a"
      },
      "source": [
        "model.fit(x=X_train,y=y_train,epochs=600,validation_data=(X_test,y_test),callbacks=[early_stop])"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/600\n",
            "14/14 [==============================] - 0s 9ms/step - loss: 0.6827 - val_loss: 0.6598\n",
            "Epoch 2/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.6501 - val_loss: 0.6294\n",
            "Epoch 3/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.6164 - val_loss: 0.5911\n",
            "Epoch 4/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.5782 - val_loss: 0.5492\n",
            "Epoch 5/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.5354 - val_loss: 0.5054\n",
            "Epoch 6/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4882 - val_loss: 0.4514\n",
            "Epoch 7/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.4381 - val_loss: 0.4012\n",
            "Epoch 8/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3964 - val_loss: 0.3562\n",
            "Epoch 9/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3597 - val_loss: 0.3215\n",
            "Epoch 10/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3197 - val_loss: 0.2880\n",
            "Epoch 11/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2947 - val_loss: 0.2636\n",
            "Epoch 12/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2706 - val_loss: 0.2415\n",
            "Epoch 13/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2510 - val_loss: 0.2247\n",
            "Epoch 14/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2342 - val_loss: 0.2075\n",
            "Epoch 15/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2210 - val_loss: 0.1946\n",
            "Epoch 16/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2068 - val_loss: 0.1851\n",
            "Epoch 17/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.1951 - val_loss: 0.1722\n",
            "Epoch 18/600\n",
            "14/14 [==============================] - 0s 7ms/step - loss: 0.1879 - val_loss: 0.1664\n",
            "Epoch 19/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.1789 - val_loss: 0.1593\n",
            "Epoch 20/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1663 - val_loss: 0.1549\n",
            "Epoch 21/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1588 - val_loss: 0.1462\n",
            "Epoch 22/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1532 - val_loss: 0.1413\n",
            "Epoch 23/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1466 - val_loss: 0.1366\n",
            "Epoch 24/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1368 - val_loss: 0.1354\n",
            "Epoch 25/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1323 - val_loss: 0.1323\n",
            "Epoch 26/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1254 - val_loss: 0.1327\n",
            "Epoch 27/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.1196 - val_loss: 0.1231\n",
            "Epoch 28/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1156 - val_loss: 0.1231\n",
            "Epoch 29/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1126 - val_loss: 0.1248\n",
            "Epoch 30/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1086 - val_loss: 0.1154\n",
            "Epoch 31/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1040 - val_loss: 0.1181\n",
            "Epoch 32/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1002 - val_loss: 0.1134\n",
            "Epoch 33/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0994 - val_loss: 0.1116\n",
            "Epoch 34/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0959 - val_loss: 0.1111\n",
            "Epoch 35/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0915 - val_loss: 0.1117\n",
            "Epoch 36/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0903 - val_loss: 0.1135\n",
            "Epoch 37/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0922 - val_loss: 0.1066\n",
            "Epoch 38/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0864 - val_loss: 0.1111\n",
            "Epoch 39/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0830 - val_loss: 0.1072\n",
            "Epoch 40/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0842 - val_loss: 0.1046\n",
            "Epoch 41/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0866 - val_loss: 0.1122\n",
            "Epoch 42/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0781 - val_loss: 0.1072\n",
            "Epoch 43/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0766 - val_loss: 0.1068\n",
            "Epoch 44/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0750 - val_loss: 0.1066\n",
            "Epoch 45/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0738 - val_loss: 0.1073\n",
            "Epoch 46/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0731 - val_loss: 0.1017\n",
            "Epoch 47/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0726 - val_loss: 0.1103\n",
            "Epoch 48/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0709 - val_loss: 0.1021\n",
            "Epoch 49/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0701 - val_loss: 0.1064\n",
            "Epoch 50/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0681 - val_loss: 0.1032\n",
            "Epoch 51/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0673 - val_loss: 0.1107\n",
            "Epoch 52/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0697 - val_loss: 0.0995\n",
            "Epoch 53/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0669 - val_loss: 0.1100\n",
            "Epoch 54/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0656 - val_loss: 0.1009\n",
            "Epoch 55/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0677 - val_loss: 0.1017\n",
            "Epoch 56/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0641 - val_loss: 0.1054\n",
            "Epoch 57/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0656 - val_loss: 0.1013\n",
            "Epoch 58/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0650 - val_loss: 0.1053\n",
            "Epoch 59/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0669 - val_loss: 0.1041\n",
            "Epoch 60/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0670 - val_loss: 0.1019\n",
            "Epoch 61/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0617 - val_loss: 0.1029\n",
            "Epoch 62/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0602 - val_loss: 0.1048\n",
            "Epoch 63/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0620 - val_loss: 0.1061\n",
            "Epoch 64/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0607 - val_loss: 0.1050\n",
            "Epoch 65/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0583 - val_loss: 0.1037\n",
            "Epoch 66/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0605 - val_loss: 0.1125\n",
            "Epoch 67/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0671 - val_loss: 0.1062\n",
            "Epoch 68/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0611 - val_loss: 0.1026\n",
            "Epoch 69/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0583 - val_loss: 0.1093\n",
            "Epoch 70/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0571 - val_loss: 0.1001\n",
            "Epoch 71/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0606 - val_loss: 0.1070\n",
            "Epoch 72/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0556 - val_loss: 0.1024\n",
            "Epoch 73/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0558 - val_loss: 0.1092\n",
            "Epoch 74/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0551 - val_loss: 0.1036\n",
            "Epoch 75/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0545 - val_loss: 0.1095\n",
            "Epoch 76/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0543 - val_loss: 0.1078\n",
            "Epoch 77/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0543 - val_loss: 0.1075\n",
            "Epoch 00077: early stopping\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7efe88671b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dk8vADS6sjQu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#SO basically what early_stop allows us is that we can pick any arbitrarily large number of epochs. \n",
        "#and, then indicate that we want the iteratons to stop early, based on when the loss no longer minimizes further. "
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JhqPYkz5uFce",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loss_after_early_stop = pd.DataFrame(model.history.history)"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GgEzvFj5uRn_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "835684a2-6a52-4b44-f41a-654eb7e2cdff"
      },
      "source": [
        "loss_after_early_stop.plot()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efe88717828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wOOGsZvUutmz",
        "colab_type": "text"
      },
      "source": [
        "#Another method to Prevent OVERFITTING - DropOut Layers\n",
        "\n",
        "## -> Turning OFF a % of neurons randomly"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "84tsBSdouVMt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import Dropout"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3otLSMiIvI3s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#re-make the model and compile it again. \n",
        "#30 Features. \n",
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(30,activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(15,activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "#BINARY CLASS. SO FINAL OUTPUT SHOULD BE 0/1\n",
        "model.add(Dense(1,activation='sigmoid'))\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam')"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EY4whIe4vPgf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "55bd280a-c0fe-4ea3-bd9d-d10c5d37a14c"
      },
      "source": [
        "model.fit(x=X_train,y=y_train,epochs=600,validation_data=(X_test,y_test),callbacks=[early_stop])"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/600\n",
            "14/14 [==============================] - 0s 9ms/step - loss: 0.7381 - val_loss: 0.7038\n",
            "Epoch 2/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.7272 - val_loss: 0.6907\n",
            "Epoch 3/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.7173 - val_loss: 0.6780\n",
            "Epoch 4/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.6961 - val_loss: 0.6656\n",
            "Epoch 5/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.6562 - val_loss: 0.6539\n",
            "Epoch 6/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.6704 - val_loss: 0.6409\n",
            "Epoch 7/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.6502 - val_loss: 0.6258\n",
            "Epoch 8/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.6198 - val_loss: 0.6052\n",
            "Epoch 9/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.6180 - val_loss: 0.5869\n",
            "Epoch 10/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.6000 - val_loss: 0.5660\n",
            "Epoch 11/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.5719 - val_loss: 0.5431\n",
            "Epoch 12/600\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.5636 - val_loss: 0.5092\n",
            "Epoch 13/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.5236 - val_loss: 0.4771\n",
            "Epoch 14/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.5139 - val_loss: 0.4304\n",
            "Epoch 15/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4781 - val_loss: 0.3955\n",
            "Epoch 16/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.4476 - val_loss: 0.3663\n",
            "Epoch 17/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4375 - val_loss: 0.3462\n",
            "Epoch 18/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4045 - val_loss: 0.3249\n",
            "Epoch 19/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.4153 - val_loss: 0.3022\n",
            "Epoch 20/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3503 - val_loss: 0.2836\n",
            "Epoch 21/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3414 - val_loss: 0.2608\n",
            "Epoch 22/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3520 - val_loss: 0.2464\n",
            "Epoch 23/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3534 - val_loss: 0.2338\n",
            "Epoch 24/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3118 - val_loss: 0.2204\n",
            "Epoch 25/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3163 - val_loss: 0.2105\n",
            "Epoch 26/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3003 - val_loss: 0.2018\n",
            "Epoch 27/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3125 - val_loss: 0.1935\n",
            "Epoch 28/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2819 - val_loss: 0.1853\n",
            "Epoch 29/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2811 - val_loss: 0.1843\n",
            "Epoch 30/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2916 - val_loss: 0.1782\n",
            "Epoch 31/600\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2586 - val_loss: 0.1719\n",
            "Epoch 32/600\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2901 - val_loss: 0.1697\n",
            "Epoch 33/600\n",
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            "Epoch 185/600\n",
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            "Epoch 186/600\n",
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            "Epoch 187/600\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0897 - val_loss: 0.1127\n",
            "Epoch 00187: early stopping\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7efe8883e2e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWN63xnOwJMb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_loss_after_ES_DO = pd.DataFrame(model.history.history)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qt-mxc5Swa53",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "b8989664-d8f8-46ac-9462-d82924fd33a5"
      },
      "source": [
        "model_loss_after_ES_DO.plot()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efe88d925c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idKRFVm2wdeH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "d71a9814-80ac-4d24-f4db-4fcb3c1d5161"
      },
      "source": [
        "yp = model.predict_classes(X_test)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-56-fa892ad922ae>:1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
            "Instructions for updating:\n",
            "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tPbQ3BGDyIfs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KWHF4m5oyToj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "a3f3cbaf-8ab3-4322-b7b1-85908323887d"
      },
      "source": [
        "print(\"\\n This is the Classification Report \\n \")\n",
        "print(classification_report(y_test,yp))\n",
        "\n",
        "print(\"\\n ************** \\n\")\n",
        "\n",
        "print(\"\\n This is the Confusion Matrix \\n \")\n",
        "print(confusion_matrix(y_test,yp))"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " This is the Classification Report \n",
            " \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.98      0.96        55\n",
            "           1       0.99      0.95      0.97        88\n",
            "\n",
            "    accuracy                           0.97       143\n",
            "   macro avg       0.96      0.97      0.96       143\n",
            "weighted avg       0.97      0.97      0.97       143\n",
            "\n",
            "\n",
            " ************** \n",
            "\n",
            "\n",
            " This is the Confusion Matrix \n",
            " \n",
            "[[54  1]\n",
            " [ 4 84]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EbmehQwEyuNm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}